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Found 9 papers, 4 papers with code
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs.
Representation Engineering: A Top-Down Approach to AI Transparency
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience.
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Diffeomorphic Counterfactuals with Generative Models
Counterfactuals can explain classification decisions of neural networks in a human interpretable way.
Automated Dissipation Control for Turbulence Simulation with Shell Models
The application of machine learning (ML) techniques, especially neural networks, has seen tremendous success at processing images and language.
Diffeomorphic Explanations with Normalizing Flows
Normalizing flows are diffeomorphisms which are parameterized by neural networks.
Towards Robust Explanations for Deep Neural Networks
Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks.
Fairwashing Explanations with Off-Manifold Detergent
Explanation methods promise to make black-box classifiers more transparent.
Explanations can be manipulated and geometry is to blame
Explanation methods aim to make neural networks more trustworthy and interpretable.
CNN Cascades for Segmenting Whole Slide Images of the Kidney
Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems.
